CN114303996A - Observability analysis method based on intelligent detection - Google Patents
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Abstract
The invention discloses an observability analysis method based on intelligent detection, which adopts an observability analysis system and comprises an information acquisition module, a data analysis module and a result processing module, wherein the information acquisition module is electrically connected with the data analysis module, the data analysis module is electrically connected with the result processing module, the information acquisition module is used for acquiring and recording information related to a pet dog, the data analysis module is used for calculating and analyzing the acquired data information, the result processing module is used for processing the calculation and analysis result, the information acquisition module comprises a detection module, a time recording module, an amplitude measuring module and an identification module, the detection module is electrically connected with the time recording module, the amplitude measuring module is electrically connected with the detection module, the time recording module is electrically connected with the amplitude measuring module, the invention has the characteristic of correctly controlling the food intake according to the body needs of the pet dog.
Description
Technical Field
The invention relates to the technical field of observability analysis, in particular to an observability analysis method based on intelligent detection.
Background
People in modern society are more and more independent, aging is more and more serious, and pets are the hosts of human feelings and can effectively meet the psychological needs of people. By caring and playing with the pet, the pet toy can bring much joy to people and can also enable people to relax effectively, and the occurrence of various chronic diseases is related to stress depression. Therefore, the pet raising method is not only pleasant but also helpful for reducing the occurrence of chronic diseases, so that more and more families are raising pets.
The pet dog is favored by many pet families due to mild character, clever and lively. For how to feed the pet dog and control the food feeding amount of the pet dog, many users judge through own experience, for example, by watching the feeding state of the pet dog, the pet dog quickly feels hungry, so that the pet dog is fed in a large amount, which is completely wrong, because the eating speed does not necessarily indicate that the pet dog is hungry, but a diet habit evolved for a long time of the pet dog can lead the pet dog to be too fat in the long term; or the pet dog is fed according to the body type and the type of the pet dog by watching the instruction attached when the pet dog purchases dog food, but the pet dog is lively and has uncontrollable behavior, and the normal body requirement of the pet dog cannot be met by feeding according to the instruction; the pet dog is too fat or too thin and weak, so that diseases are easily caused, the pet dog owner needs to spend a large amount of time and money for treatment if the pet dog is light, and the pet dog is seriously threatened and cannot save lives if the pet dog is too thin and weak. Therefore, it is necessary to design an observability analysis method based on intelligent detection, which can correctly control the food intake according to the needs of the pet dog body.
Disclosure of Invention
The invention aims to provide an observability analysis method based on intelligent detection so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an observability analysis method based on intelligent detection adopts an observability analysis system, comprises an information acquisition module, a data analysis module and a result processing module, and is characterized in that: the pet dog information acquisition module is electrically connected with the data analysis module, the data analysis module is electrically connected with the result processing module, the information acquisition module is used for acquiring and recording pet dog related information, the data analysis module is used for calculating and analyzing the acquired data information, and the result processing module is used for processing the calculation and analysis result;
the pet dog detection system comprises an information acquisition module, a time recording module, an amplitude measurement module and an identification module, wherein the detection module is electrically connected with the time recording module, the amplitude measurement module is electrically connected with the detection module, the time recording module is electrically connected with the amplitude measurement module, the detection module is used for detecting a pet dog, the detection module comprises an infrared induction unit and a high-definition camera unit, the infrared induction unit is electrically connected with the high-definition camera unit, the infrared induction unit is used for performing infrared induction detection, the high-definition camera unit is used for performing high-definition camera shooting and image acquisition, the time recording module is used for recording relevant time, the amplitude measurement module is used for measuring the breathing amplitude of the pet dog, and the identification module is used for identifying the belly of the pet dog.
According to the technical scheme, the data analysis module comprises a calculation analysis module, a judgment module, a data receiving module, a scanning module, a height difference module and an adjustment module, the calculation analysis module is electrically connected with the data receiving module, the scanning module is electrically connected with the height difference module, the height difference module is electrically connected with the data receiving module, the calculation analysis module is used for calculating and analyzing data information, the judgment module is used for judging and analyzing relevant states, the data receiving module is used for receiving data, the scanning module is used for scanning the abdominal contour of the pet dog, the height difference module is used for calculating and analyzing the height difference data, and the adjustment module is used for adjusting the relevant data.
According to the technical scheme, the result processing module comprises a prompting module and an information sending module, the prompting module is electrically connected with the information sending module, the prompting module is used for prompting information to a user, and the information sending module is used for sending information data.
According to the technical scheme, the observability analysis method based on intelligent detection mainly comprises the following steps:
step S1: installing a camera device at a feeding position of the pet dog to acquire and detect information;
step S2: when the pet dog needs to eat, the information acquisition module acquires data information of the pet dog state and the amount of exercise before eating;
step S3: the data analysis module calculates and analyzes the optimal food intake of the pet dog according to the data information acquisition result;
step S4: and the result processing module sends the calculation analysis result data to the user mobile phone end for reference use by the user.
According to the above technical solution, the step S1 further includes the following steps:
step S11: the infrared sensing unit arranged at the feeding position of the pet dog senses that the heat source is close to the pet dog, and the high-definition camera unit is started through an electric signal;
step S12: after the high-definition camera unit determines that the heat source is a pet dog, starting an identification module;
step S13: the identification module identifies and records the abdomen of the pet dog image acquired by the high-definition camera unit;
step S14: the pet dog collar records the amount of pet dog motion between meals.
According to the above technical solution, the step S14 further includes the following steps:
step S141: the amplitude measuring module measures the neck shrugging amplitude of the pet dog;
step S142: the amplitude measuring module draws the pet dog neck shrug amplitude as an amplitude coordinate curve, detects the change of the curve in real time, when the pet dog neck shrug amplitude curve is detected to rapidly reach the threshold value W with the slope change larger than the standard value N, the time recording submodule starts to record the pet dog neck shrug time to obtain the pet dog neck shrug time as T, and when the pet dog neck shrug amplitude curve is detected to slowly reach the threshold value W with the slope change smaller than or equal to the change standard value N, the time recording submodule is not started to record the pet dog neck shrug time;
step S143: the adjusting module acquires that the local environment temperature is C through a network signal, and adjusts a threshold value W along with the change of the environment temperature, wherein W is CX, X is a temperature conversion coefficient, and the adjusting range of the threshold value W is Wmin≤W≤Wmax。
According to the above technical solution, the step S2 further includes the following steps:
step S21: the data receiving module receives the identified abdomen image of the pet dog and starts the scanning module through an electric signal;
step S22: the scanning module scans the part of the abdomen image of the pet dog, which is intersected by the color depth, to obtain an abdomen contour curve of the pet dog;
step S23: and the height difference module measures the height difference between the highest position and the lowest position of the scanning result profile curve to obtain the height difference H.
According to the above technical solution, the step S3 further includes the following steps:
step S31: after the calculation analysis module acquires the acquired information data through the electric signals, calculating and analyzing to obtain that the food intake of the pet dog is J;
step S32: the judging module compares the height difference of the abdomen contour curve of the pet dog with a body type threshold value P to judge the obesity degree of the pet dog;
step S33: when H is larger than or equal to 2P, the judgment module judges that the pet dog is too thin and weak; when H is less than or equal to P, the judgment module judges that the pet dog is too fat, and when P is less than H and less than 2P, the judgment module judges that the pet dog is in a normal state.
According to the technical scheme, the formula for calculating the current food intake J of the pet dog in the step S31 is as follows:
wherein J is the current food intake of the pet dog, K is the food intake conversion coefficient, H is the height difference of the abdomen contour curve of the pet dog, P is the body type threshold value, T is the gasping time of the pet dog, and Q is the dosage required by the dog food specification.
According to the above technical solution, the step S4 further includes the following steps:
step S41: the information sending module sends result data to the user through a network signal after obtaining a calculation analysis result through an electric signal, so that the user can provide a reference basis when feeding the pet dog;
step S42: the prompting module sends out prompting sound to prompt the user to feed the pet dog.
Compared with the prior art, the invention has the following beneficial effects: according to the pet dog food intake control system, the information acquisition module, the data analysis module and the result processing module are arranged, the food intake can be correctly controlled according to the body shape and the exercise amount of the pet dog, the body health of the pet dog is effectively protected, the threshold value W is adjusted along with the temperature change through the adjustment module, the result that the threshold value W is easily triggered due to overhigh temperature can be eliminated, the information acquisition calculation result is more accurate, meanwhile, the infrared sensing unit and the high-definition camera unit are arranged, objects entering the detection range can be intelligently identified, and the system calculation force is saved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of the system module composition of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: an observability analysis method based on intelligent detection adopts an observability analysis system, comprises an information acquisition module, a data analysis module and a result processing module, and is characterized in that: the information acquisition module is electrically connected with the data analysis module, the data analysis module is electrically connected with the result processing module, the information acquisition module is used for acquiring and recording information related to the pet dog, the data analysis module is used for calculating and analyzing the acquired data information, and the result processing module is used for processing the calculation and analysis result;
the information acquisition module comprises a detection module, a time recording module, an amplitude measuring module and an identification module, the detection module is electrically connected with the time recording module, the amplitude measuring module is electrically connected with the detection module, the time recording module is electrically connected with the amplitude measuring module, the detection module is used for detecting the pet dog, the detection module comprises an infrared induction unit and a high-definition camera unit, the infrared induction unit is electrically connected with the high-definition camera unit, the infrared induction unit is used for performing infrared induction detection, the high-definition camera unit is used for performing high-definition camera shooting and image acquisition, the time recording module is used for recording relevant time, the amplitude measuring module is used for measuring the breathing amplitude of the pet dog, and the identification module is used for identifying the belly of the pet dog.
The data analysis module comprises a calculation analysis module, a judgment module, a data receiving module, a scanning module, a height difference module and an adjustment module, wherein the calculation analysis module is electrically connected with the data receiving module, the scanning module is electrically connected with the height difference module, the height difference module is electrically connected with the data receiving module, the calculation analysis module is used for calculating and analyzing data information, the judgment module is used for judging and analyzing related states, the data receiving module is used for receiving data, the scanning module is used for scanning the outline of the abdomen of the pet dog, the height difference module is used for calculating and analyzing height difference data, and the adjustment module is used for adjusting related data.
The result processing module comprises a prompting module and an information sending module, the prompting module is electrically connected with the information sending module, the prompting module is used for prompting information to a user, and the information sending module is used for sending information data.
An observability analysis method based on intelligent detection mainly comprises the following steps:
step S1: installing a camera device at a feeding position of the pet dog to acquire and detect information;
step S2: when the pet dog needs to eat, the information acquisition module acquires data information of the pet dog state and the amount of exercise before eating;
step S3: the data analysis module calculates and analyzes the optimal food intake of the pet dog according to the data information acquisition result;
step S4: and the result processing module sends the calculation analysis result data to the user mobile phone end for reference use by the user.
Step S1 further includes the steps of:
step S11: the infrared sensing unit arranged at the feeding position of the pet dog senses that the heat source is close to the pet dog, and the high-definition camera unit is started through an electric signal; when the feeding time is about to come, the pet dog waits for feeding of food in the feeding area in advance due to conditioned reflex, and the high-definition camera unit is started after the pet dog is detected by the infrared sensing unit, so that the system computing power can be saved;
step S12: after the high-definition camera unit determines that the heat source is a pet dog, starting an identification module;
step S13: the identification module identifies and records the abdomen of the pet dog image acquired by the high-definition camera unit; the abdomen of the pet dog can accurately reflect the obesity degree of the pet dog, and at the moment, the abdomen image of the pet dog is identified and recorded to prepare for the follow-up analysis of the obesity degree of the pet dog;
step S14: the pet dog collar records the amount of pet dog motion between meals.
Step S14 further includes the steps of:
step S141: the amplitude measuring module measures the neck shrugging amplitude of the pet dog; because sweat glands do not exist in the pet dog, heat generated by the body of the pet dog after sports is removed by opening the mouth and whetting the mouth widely, the neck of the pet dog is shrunken and changed along with the breathing of the pet dog, although the exercise amount of the pet dog can be detected through heart rate change, the heart rate of the pet dog is still changed when the pet dog is subjected to scaring factors, and the method for detecting the exercise amount of the pet dog through the heart rate change is inaccurate;
step S142: the amplitude measuring module draws the pet dog neck shrug amplitude as an amplitude coordinate curve, detects the change of the curve in real time, when the pet dog neck shrug amplitude curve is detected to rapidly reach the threshold value W with the slope change larger than the standard value N, the time recording submodule starts to record the pet dog neck shrug time to obtain the pet dog neck shrug time as T, and when the pet dog neck shrug amplitude curve is detected to slowly reach the threshold value W with the slope change smaller than or equal to the change standard value N, the time recording submodule is not started to record the pet dog neck shrug time; when the pet dog dissipates heat through wheezing due to movement, the neck shrugging amplitude curve is quickly increased to a threshold value W; when the neck shrug amplitude curve of the pet dog slowly rises to the threshold value W, the result is shown that the pet dog is caused by breathing and heat dissipation due to overhigh ambient temperature;
step S143: the adjusting module acquires that the local environment temperature is C through a network signal, and adjusts a threshold value W along with the change of the environment temperature, wherein W is CX, X is a temperature conversion coefficient, and the adjusting range of the threshold value W is Wmin not more than Wmax(ii) a By adjusting the threshold value W along with the temperature change, the result that the threshold value W is easily triggered due to overhigh temperature can be eliminated, so that the information acquisition and calculation result is more accurate.
Step S2 further includes the steps of:
step S21: the data receiving module receives the identified abdomen image of the pet dog and starts the scanning module through an electric signal;
step S22: the scanning module scans the part of the abdomen image of the pet dog, which is intersected by the color depth, to obtain an abdomen contour curve of the pet dog; hairs grow on the abdomen of the pet dog but the quantity of the hairs is small, and the color of the whole body is lighter than that of the side surface of the abdomen, so that the part where the colors are dark and light is the outline curve of the abdomen of the pet dog;
step S23: the height difference module measures the height difference between the highest position and the lowest position of the scanning result profile curve to obtain the height difference H; when the pet dog is fattened, the abdomen is larger and the whole contour is closer to smoothness, and the height difference of the abdomen contour curve is smaller; conversely, the greater the difference in height of the abdomen contour curve, the smaller the abdomen and the closer the overall contour approaches steepness as the pet dog becomes leaner.
Step S3 further includes the steps of:
step S31: after the calculation analysis module acquires the acquired information data through the electric signals, calculating and analyzing to obtain that the food intake of the pet dog is J;
step S32: the judging module compares the height difference of the abdomen contour curve of the pet dog with a body type threshold value P to judge the obesity degree of the pet dog;
step S33: when H is larger than or equal to 2P, the judgment module judges that the pet dog is too thin and weak; when H is less than or equal to P, the judgment module judges that the pet dog is too fat, and when P is less than H and less than 2P, the judgment module judges that the pet dog is in a normal state.
The formula for calculating the food intake J of the pet dog in the step S31 is as follows:
wherein J is the current food intake of the pet dog, K is a food intake conversion coefficient, H is a height difference value of an abdomen contour curve of the pet dog, P is a body type threshold value, T is gasping time of the pet dog, and Q is a dosage required by a dog food specification; as can be seen from the formula, with the same amount of motion: the height difference of the abdomen contour curve of the pet dog with the thin body is large, and the more energy is consumed, the more food needs to be eaten, and the food intake of the pet dog is calculated and analyzed by adopting a first formula; the fat pet dog has smaller height difference of the abdomen contour curve and consumes less energy. Therefore, the less food needs to be eaten, the food intake is calculated and analyzed by a third formula; and the height difference of the abdomen contour curve of the pet dog with normal body shape is moderate, so the food intake of the pet dog is calculated and analyzed by adopting a second formula.
Step S4 further includes the steps of:
step S41: the information sending module sends result data to the user through a network signal after obtaining a calculation analysis result through an electric signal, so that the user can provide a reference basis when feeding the pet dog;
step S42: the prompting module sends out prompting sound to prompt the user to feed the pet dog.
Example (b): install the infrared induction unit response at pet dog feed position and detect that there is the heat source to be close to, confirm the heat source through high definition camera unit for the pet dog after, identification module carries out the discernment record to the pet dog image belly that high definition camera unit gathered, and obtain the pet dog neck ring record feed back last time and this feed before the exercise amount of the pet dog between the two: the pet dog neck part shrug and moving amplitude is measured by the amplitude measuring module, the pet dog neck part shrug and moving amplitude is drawn as an amplitude coordinate curve, the curve change is detected in real time, when the situation that the slope change of the pet dog neck part shrug and moving amplitude curve is larger than a standard value 1.8 and quickly reaches a threshold value W is detected, the time recording submodule starts to record the pet dog neck part shrug and moving time, the obtained pet dog neck part shrug and moving time is T-0.1 h, the adjusting module obtains that the local environment temperature is 25 ℃ through a network signal, the temperature conversion coefficient X is 0.2, and the threshold value W is 0.2 multiplied by 25 which is 5; the scanning module scans a part where an image of the abdomen of the pet dog is in a dark and light boundary to obtain an abdomen contour curve of the pet dog, the height difference module measures the height difference between the highest part and the lowest part of the contour curve of the scanning result, the height difference is H10 cm, the height difference is compared with a body type threshold value P5, the judgment module judges that the pet dog is too thin and weak because H is larger than or equal to 2P, and the feed conversion coefficient K is 20, so that the formula I is adopted to calculate that the feed of the pet dog is J20 x (10+0.1) 202g, the information sending module sends result data to a user through a network signal after acquiring a calculation and analysis result through an electric signal, the result data is provided for the user to provide a reference basis when the pet dog is fed, and the prompting module sends a prompting sound to prompt the user to feed the pet dog.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An observability analysis method based on intelligent detection adopts an observability analysis system, comprises an information acquisition module, a data analysis module and a result processing module, and is characterized in that: the pet dog information acquisition module is electrically connected with the data analysis module, the data analysis module is electrically connected with the result processing module, the information acquisition module is used for acquiring and recording pet dog related information, the data analysis module is used for calculating and analyzing the acquired data information, and the result processing module is used for processing the calculation and analysis result;
the pet dog detection system comprises an information acquisition module, a time recording module, an amplitude measurement module and an identification module, wherein the detection module is electrically connected with the time recording module, the amplitude measurement module is electrically connected with the detection module, the time recording module is electrically connected with the amplitude measurement module, the detection module is used for detecting a pet dog, the detection module comprises an infrared induction unit and a high-definition camera unit, the infrared induction unit is electrically connected with the high-definition camera unit, the infrared induction unit is used for performing infrared induction detection, the high-definition camera unit is used for performing high-definition camera shooting and image acquisition, the time recording module is used for recording relevant time, the amplitude measurement module is used for measuring the breathing amplitude of the pet dog, and the identification module is used for identifying the belly of the pet dog.
2. An observability analysis method based on intelligent detection according to claim 1, wherein: the data analysis module comprises a calculation analysis module, a judgment module, a data receiving module, a scanning module, a height difference module and an adjustment module, wherein the calculation analysis module is electrically connected with the data receiving module, the scanning module is electrically connected with the height difference module, the height difference module is electrically connected with the data receiving module, the calculation analysis module is used for calculating and analyzing data information, the judgment module is used for judging and analyzing related states, the data receiving module is used for receiving data, the scanning module is used for scanning the abdominal profile of the pet dog, the height difference module is used for calculating and analyzing height difference data, and the adjustment module is used for adjusting related data.
3. An observability analysis method based on intelligent detection according to claim 2, wherein: the result processing module comprises a prompting module and an information sending module, the prompting module is electrically connected with the information sending module, the prompting module is used for prompting information to a user, and the information sending module is used for sending information data.
4. A method of observability analysis based on intelligent detection according to claim 3, wherein: the observability analysis method based on intelligent detection mainly comprises the following steps:
step S1: installing a camera device at a feeding position of the pet dog to acquire and detect information;
step S2: when the pet dog needs to eat, the information acquisition module acquires data information of the pet dog state and the amount of exercise before eating;
step S3: the data analysis module calculates and analyzes the optimal food intake of the pet dog according to the data information acquisition result;
step S4: and the result processing module sends the calculation analysis result data to the user mobile phone end for reference use by the user.
5. An observability analysis method based on intelligent detection according to claim 4, wherein: the step S1 further includes the steps of:
step S11: the infrared sensing unit arranged at the feeding position of the pet dog senses that the heat source is close to the pet dog, and the high-definition camera unit is started through an electric signal;
step S12: after the high-definition camera unit determines that the heat source is a pet dog, starting an identification module;
step S13: the identification module identifies and records the abdomen of the pet dog image acquired by the high-definition camera unit;
step S14: the pet dog collar records the amount of pet dog motion between meals.
6. An observability analysis method based on intelligent detection according to claim 5, wherein: the step S14 further includes the steps of:
step S141: the amplitude measuring module measures the neck shrugging amplitude of the pet dog;
step S142: the amplitude measuring module draws the pet dog neck shrug amplitude as an amplitude coordinate curve, detects the change of the curve in real time, when the pet dog neck shrug amplitude curve is detected to rapidly reach the threshold value W with the slope change larger than the standard value N, the time recording submodule starts to record the pet dog neck shrug time to obtain the pet dog neck shrug time as T, and when the pet dog neck shrug amplitude curve is detected to slowly reach the threshold value W with the slope change smaller than or equal to the change standard value N, the time recording submodule is not started to record the pet dog neck shrug time;
step S143: the adjusting module acquires that the local environment temperature is C through a network signal, and adjusts a threshold value W along with the change of the environment temperature, wherein W is CX, X is a temperature conversion coefficient, and the adjusting range of the threshold value W is Wmin≤W≤Wmax。
7. An observability analysis method based on intelligent detection according to claim 6, wherein: the step S2 further includes the steps of:
step S21: the data receiving module receives the identified abdomen image of the pet dog and starts the scanning module through an electric signal;
step S22: the scanning module scans the part of the abdomen image of the pet dog, which is intersected by the color depth, to obtain an abdomen contour curve of the pet dog;
step S23: and the height difference module measures the height difference between the highest position and the lowest position of the scanning result profile curve to obtain the height difference H.
8. An observability analysis method based on intelligent detection according to claim 7, wherein: the step S3 further includes the steps of:
step S31: after the calculation analysis module acquires the acquired information data through the electric signals, calculating and analyzing to obtain that the food intake of the pet dog is J;
step S32: the judging module compares the height difference of the abdomen contour curve of the pet dog with a body type threshold value P to judge the obesity degree of the pet dog;
step S33: when H is larger than or equal to 2P, the judgment module judges that the pet dog is too thin and weak; when H is less than or equal to P, the judgment module judges that the pet dog is too fat, and when P is less than H and less than 2P, the judgment module judges that the pet dog is in a normal state.
9. An observability analysis method based on intelligent detection according to claim 8, wherein: the formula for calculating the food intake J of the pet dog in the step S31 is as follows:
wherein J is the current food intake of the pet dog, K is the food intake conversion coefficient, H is the height difference of the abdomen contour curve of the pet dog, P is the body type threshold value, T is the gasping time of the pet dog, and Q is the dosage required by the dog food specification.
10. An observability analysis method based on intelligent detection according to claim 9, wherein: the step S4 further includes the steps of:
step S41: the information sending module sends result data to the user through a network signal after obtaining a calculation analysis result through an electric signal, so that the user can provide a reference basis when feeding the pet dog;
step S42: the prompting module sends out prompting sound to prompt the user to feed the pet dog.
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| CN104077495A (en) * | 2014-07-17 | 2014-10-01 | 杜晓松 | Wearable human body feature information collecting and monitoring system |
| CN107635509A (en) * | 2015-02-27 | 2018-01-26 | 因吉纳瑞股份公司 | For determining the improved method and relevant device of Body Condition Score, body weight and fertility status |
| CN107728552A (en) * | 2017-09-06 | 2018-02-23 | 上海斐讯数据通信技术有限公司 | The feeding method and system of a kind of pet |
| CN108668932A (en) * | 2018-04-27 | 2018-10-19 | 扬州圣林弹簧五金有限公司 | Intelligent animals food throwing machine |
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